Director, Product Engineering ((AI POD Lead)

About the Role
DTEX is building durable, cross-functional product PODs that own end-to-end customer outcomes—from concept to production and real-world impact.
We are seeking a Director, Product Engineering to lead our AI POD, responsible for defining how enterprises understand, detect, and mitigate risk in the age of AI.

This is a category-defining role at the intersection of cybersecurity, behavioral analytics, and applied AI. You will lead a dedicated, cross-functional team to build capabilities that address emerging risks from how humans interact with AI systems, models, and data—at enterprise scale.

This is not an incremental product area. You will be tackling problems such as:

• Misuse of generative AI and copilots in the enterprise
• Data leakage through prompts and AI-assisted workflows
• Behavioral anomalies across human + AI interaction patterns
• Emerging attack vectors including model manipulation, distillation, and insider-enabled AI risk

What You’ll Do

Own Outcomes, Not Just Delivery

• Own adoption, impact, and success of the AI pillar
• Define and drive the product strategy and roadmap aligned to DTEX’s platform vision
• Translate ambiguous, emerging problems into clear product direction and execution

Lead Cross-Functional Execution

• Operate a high-velocity POD model with engineering, product, design, and domain specialists
• Drive execution cadence, release planning, and milestone delivery
• Remove dependencies and ensure the team can ship quickly and predictably

Build AI-Native Product Capabilities

• Define and evolve the AI architecture for the pillar (e.g., behavioral analytics, anomaly detection, LLM-driven reasoning, signal fusion)
• Drive decisions on build vs. leverage vs. partner across models, infrastructure, and data pipelines
• Ensure systems are production-grade—observable, explainable, and privacy-preserving
• Rapidly iterate from data → insight → model → product capability

Integrate with Go-to-Market

• Partner with Sales, Customer Success, and Marketing to bring new capabilities to market
• Shape POVs, customer narratives, and early adoption strategies
• Incorporate real-world customer feedback into product direction without introducing churn

Ensure Quality and Operational Scale

• Deliver solutions that are stable, scalable, and enterprise-ready
• Uphold strong engineering practices across reliability, performance, and deployment
• Track and improve delivery effectiveness (e.g., lead time, deployment frequency, iteration velocity)

What You’ll Work With

• Enterprise-scale behavioral telemetry across users, data, and systems
• A privacy-preserving, metadata-first architecture designed for regulated environments
• Real-world deployments across financial services, government, and global enterprises
• A platform that connects human behavior, data sensitivity, and AI interaction patterns

What Success Looks Like

• Measurable customer adoption and impact of AI capabilities
• High-quality detection of AI-related risks (precision, recall, and explainability)
• Rapid iteration cycles from new signals to production features
• Predictable, high-velocity delivery with minimal cross-team dependency
• Strong alignment between product innovation and market adoption

Required Qualifications

• Proven experience leading cross-functional product and engineering teams to deliver high-impact outcomes
• Strong technical background with the ability to guide architecture-level decisions
• Experience building or deploying production AI/ML systems (e.g., behavioral models, anomaly detection, LLM-based systems, or data-driven platforms)
• Ability to operate in high-ambiguity, rapidly evolving technical domains
• Demonstrated use of AI to accelerate both product capabilities and development workflows
• Strong communication and executive alignment skills

Preferred Qualifications

• Experience in one or more of:
  • AI security or model risk
  • Behavioral analytics / UEBA
  • Data protection or modern DLP systems
• Familiarity with:
  • LLM ecosystems and agentic architectures
  • Signal processing and detection pipelines
  • Enterprise SaaS product development at scale

What This Role Is Not

• Not a project management role
• Not a handoff between product and engineering
• Not focused on output over outcomes

Why Join DTEX

• Define a new category – Help establish AI as a core enterprise security discipline
• Work on real problems at scale – Production deployments where AI risk is already a board-level concern
• End-to-end ownership – Build from first principles and see your work directly impact customers
• Trusted AI systems – Develop solutions that balance detection power with privacy and regulatory constraints
• High-impact environment – Join a team tackling some of the most complex challenges in modern security